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Wireless Personal Communications
Erschienen in: Wireless Personal Communications 3/2022

30.10.2021

A Comprehensive Survey of Detection of Tampered Video and Localization of Tampered Frame

verfasst von: T. Anbu, M. Milton Joe, G. Murugeswari

Erschienen in: Wireless Personal Communications | Ausgabe 3/2022

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Abstract

In today’s world most of the people are becoming more and more dependent on visual media information particularly digital images and videos. However, the advancement in technology has lead to increase in number of editing tools that make the image and video tampering easier and faster. The content of the digital video can be manipulated or altered effectively with the help of such editing tools without leaving any noticeable signs. Numerous attempts have been made over the previous decade to recognize the altered videos and localization of the altered frames with high exactness dependent on some extraordinarily structured system. This paper gives a detailed review of existing methodologies for recognizing tampered videos, localization of the altered frames and reconstruction of the tampered video.
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Literatur
1.
Abbasi Aghamaleki, J., & Behrad, A. (2017). Malicious inter-frame video tampering detection in MPEG videos using time and spatial domain analysis of quantization effects. Multimedia Tools and Applications, 76(20), 20691–20717.CrossRef
2.
Abbasi Aghamaleki, J., & Behrad, A. (2016). Inter-frame video forgery detection and localization using intrinsic effects of double compression on quantization errors of video coding. Signal Processing: Image Communication, 47, 289–302.
3.
Liu, Y., Huang, T., & Liu, Y. (2018). A novel video forgery detection algorithm for blue screen compositing based on 3-stage foreground analysis and tracking. Multimedia Tools and Applications, 77(6), 7405–7427.CrossRef
4.
Farid, H. (2006). Exposing digital forgeries in scientific images. In Proceedings of the 8th workshop on multimedia and security, ACM (pp 29–36).
5.
Kingra, S., Aggarwal, N., & Singh, R. D. (2017). Inter-frame forgery detection in H. 264 videos using motion and brightness gradients. Multimedia Tools and Applications, 76(24), 25767–25786.CrossRef
6.
Singh, R. D., & Aggarwal, N. (2018). V Video content authentication techniques: A comprehensive survey. Multimedia Systems, 24(2), 211–240.CrossRef
7.
Su, L., & Li, C. (2018). A novel passive forgery detection algorithm for video region duplication. Multidimensional Systems and Signal Processing, 29(3), 1173–1190.MathSciNetCrossRef
8.
Liu, H., Li, S., & Bian, S. (2014). Detecting frame deletion in H. 264 video. In International conference on information security practice and experience, 5 May (pp. 262–270). Springer.
9.
Wang, Q., Li, Z., Zhang, Z., & Ma, Q. (2014). Video inter-frame forgery identification based on optical flow consistency. Sensors & Transducers, 166(3), 229.
10.
Fadl, S. M., Han, Q., & Li, Q. (2018). Authentication of surveillance videos: Detecting frame duplication based on residual frame. Journal of Forensic Sciences, 63(4), 1099–1109.CrossRef
11.
Milani, S., Tagliasacchi, M., & Tubaro, S. (2014). Discriminating multiple JPEG compressions using first digit features. APSIPA Transactions on Signal and Information Processing, 3, e19.CrossRef
12.
Sun, T., Wang, W., & Jiang, X. (2012). Exposing video forgeries by detecting MPEG double compression. In 2012 I.E. international conference on acoustics, speech and signal processing (ICASSP). IEEE (pp. 1389–1392).
13.
Li, Z., et al. (2016). Video inter-frame forgery identification based on the consistency of quotient of MSSIM. Security and Communication Networks, 9(17), 4548–4556.CrossRef
14.
Zhang, Z. Z., Hou, J. J., Ma, Q. L., & Li, Z. H. (2015). Efficient video frame insertion and deletion detection based on inconsistency of correlations between local binary pattern coded frames. Security and Communication Networks, 8(2), 311–320.CrossRef
15.
Chen, S., et al. (2015). Automatic detection of object-based forgery in advanced video. IEEE Transactions on Circuits and Systems for Video Technology, 26(11), 2138–2151.CrossRef
16.
Li, H., Luo, W., & Huang, J. (2012). Countering anti-JPEG compression forensics. In Proceedings of the IEEE 2012 international conference on image processing, (ICIP’2012) (pp. 241–244).
17.
Qiu, X., Li, H., Luo, W., & Huang, J. (2014) A universal image forensic strategy based on steganalytic model. In Proceedings of the 2nd ACM information hiding and multimedia security workshop (IH&MMSec’ 14), (pp. 165–170).
18.
Kodovsky, J., & Fridrich, J. (2009) Calibration revisited. In Proceedings of the 11th ACM workshop multimedia security (MMSec’09) (pp. 63–74).
19.
Pevny, T., & Fridrich, J. (2007). Merging Markov and DCT features for multiclass JPEG steganalysis. In Proceedings of the SPIE, electronic imaging, security, steganography, and watermarking of multimedia contents IX (pp. 301–304).
20.
Pevny, T., Bas, P., & Fridrich, J. (2010). Steganalysis by subtractive pixel adjacency matrix. IEEE Transactions on Information Forensics and Security, 5(2), 215–224.CrossRef
21.
J. Kodovsky, T. Pevny, and J. Fridrich, Modern steganalysis can detect YASS. in Proc. SPIE, Electronic Imaging, Security, Steganography, and Watermarking of Multimedia Contents XII, pp. 201–211, 2010.
22.
Kodovsky, J., & Fridrich, J. (2012). Ensemble classifiers for steganalysis of digital media. IEEE Transactions on Information Forensics and Security, 7(2), 432–444.CrossRef
23.
Fridrich, J., & Kodovsky, J. (2012). Rich models for steganalysis of digital images. IEEE Transactions on Information Forensics and Security, 7(3), 868–882.CrossRef
24.
Kodovsky, J., & Fridrich, J. (2012). Steganalysis of JPEG images using rich models. In Proceedings of the SPIE, electronic imaging, media watermarking, security, and forensics XIV, 83030A.
25.
Fridrich, J., Kodovsky, J., Holub, V., & Goljan, M. (2011). Breaking HUGO—the process discovery. In Proceedings of the 13th information hiding workshop (IH’2011) (pp. 85–101).
26.
Jia, S., et al. (2018). Coarse-to-fine copy-move forgery detection for video forensics. IEEE Access, 6, 25323–25335.CrossRef
27.
Yang, J., & Hauptmann, A. G. (2006). Exploring temporal consistency for video analysis and retrieval. In Proceedings of the 8th ACM international workshop on Multimedia information retrieval (p. 3342).
28.
Su, L., et al. (2017). A fast forgery detection algorithm based on exponential-Fourier moments for video region duplication. IEEE Transactions on Multimedia, 20(4), 825–840.CrossRef
29.
Lai, Y., & Huang, T. (2015). Image region copy-move of forgery detection based on Exponential-Fourier moments. Journal of Image and Graphics, 20, 1212–1221.
30.
Li, Y. (2013). Image copy-move forgery detection based on polar cosine transform and approximate nearest neighbor searching. Forensic Science International, 224, 59.CrossRef
31.
Gao, B., & Jin, Y. (2010). Detection of Image copy-move tamper Using SURF in digital forensics. In 2010 Asia-Pacific conference on information network and digital content security (pp. 58–62).
32.
Dibyendu, M. (2013). Multiresolution based Gaussian mixture model for background suppression. IEEE Transactions on Image Processing, 22(12), 5022–5035.MathSciNetCrossRef
33.
Stauffer, C., & Grimson, W. (1999). Adaptive background mixture models for real-time tracking. In Proceedings 1999 IEEE computer society conference on computer vision and pattern recognition (Vol. 2, No 3, pp. 246–252).
34.
Shujia, Y., Lijun, J., Shaohui, D., Ling, Z., Chunyu, Y., & Wenhao, Z. (2012). Power line image segmentation and extra matter recognition based on improved Otsu algorithm. IET Image Process, 6(4), 426–433.MathSciNetCrossRef
35.
Vincent, L. (1994). Fast opening functions and morphological granulometries. In Conference on image algebra and morphological image processing (pp 253–267).
36.
Wang, Z., Bovik, A. C., & Sheikh, H. R. (2004). Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600–612.CrossRef
37.
Zhang, K., & Zhang, L. (2012). Real-time compressive tracking. Computer Vision - ECCV, 2012, 864–877.
38.
Wei, W., Fan, X., Song, H., & Wang, H. (2017). Video tamper detection based on multi-scale mutual information. Multimedia Tools and Applications, 78, 1–18.
39.
Singh, P., Raman, B., & Agarwal, N. (2017). Toward encrypted video tampering detection and localization based on POB number system over cloud. IEEE Transactions on Circuits and Systems for Video Technology, 28(9), 2116–2130.CrossRef
40.
Shih, T. K., Tang, N. C., & Hwang, J.-N. (2007). Ghost shadow removal in multi-layered video inpaintinga. In 2007 IEEE international conference on multimedia and expo, IEEE (pp 1471–1474).
41.
Su, L., Huang, T., & Yang, J. (2015). A video forgery detection algorithm based on compressive sensing. Multimedia Tools and Applications, 74(17), 6641–6656.CrossRef
42.
Candes, T. (2006). Near optimal signal recovery from random projections: Universal encoding strategies. IEEE Transactions on Information Theory, 52(12), 5406–5425.MathSciNetCrossRef
43.
44.
Baraniuk, R. G. (2007). Compressive sensing [lecture notes]. IEEE Signal Processing Magazine, 24(4), 118–121.CrossRef
45.
Candès, E. J., Romberg, J., & Tao, T. (2006). Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory, 52(2), 489–509.MathSciNetCrossRef
46.
Candes, E., & Wakin, M. B. (2008). An introduction to compressive sampling. IEEE Signal Processing Magazine, 5(2), 21–23.CrossRef
47.
Tsaig, Y., & Donoho, D. L. (2004). Extensions of compressed sensing. Signal Processing, 86(3), 549–571.CrossRef
48.
D’Amiano, L., et al. (2018). A patchmatch-based dense-field algorithm for video copy–move detection and localization. IEEE Transactions on Circuits and Systems for Video Technology, 29(3), 669–682.CrossRef
49.
Cozzolino, D., Poggi, G., & Verdoliva, L. (2015). Efficient dense-field copymove forgery detection. IEEE Transactions on Information Forensics and Security, 10(11), 2284–2297.CrossRef
50.
D’Amiano, L., Cozzolino, D., Poggi, G., & Verdoliva, L. (2015). Video forgery detection and localization based on 3D PatchMatch. In IEEE international conference on multimedia and expo workshops (pp. 1–6).
51.
Amanipour, V., & Ghaemmaghami, S. (2018). Video-tampering detection and content reconstruction via self-embedding. IEEE Transactions on Instrumentation and Measurement, 67(3), 505–515.CrossRef
52.
Lienhart, R. W. (1998). Comparison of automatic shot boundary detection algorithms. Proceedings of SPIE, 3656, 290–301.CrossRef
53.
Zhang, H., Kankanhalli, A., & Smoliar, S. W. (1993). Automatic partitioning of full-motion video. Multimedia Systems, 1(1), 10–28.CrossRef
54.
Qian, Z., & Feng, G. (2010). Inpainting assisted self recovery with decreased embedding data. IEEE Signal Processing Letters, 17(11), 929–932.CrossRef
Metadaten
Titel
A Comprehensive Survey of Detection of Tampered Video and Localization of Tampered Frame
verfasst von
T. Anbu
M. Milton Joe
G. Murugeswari
Publikationsdatum
30.10.2021
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 3/2022
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-021-09227-z

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